Роль методів інтерпретації машинного навчання, зокрема SHAP, в аналізі та сегментації користувачів соціальних мереж

Abstract

This abstract discusses the application of machine learning methods for analyzing social network user data and their segmentation. The feasibility of building separate models for different user groups is substantiated. Emphasis is placed on the importance of model interpretation methods for understanding their decisions and determining feature influence. The SHAP (SHapley Additive exPlanations) method is considered in more detail as one of the promising tools for analyzing models built for user segments. The potential of using feature importance information, obtained through SHAP and other interpretation methods, to improve segment profiling and more effectively assign new data is discussed.